论文标题
纵向和事件时间数据的时变双变量copula关节
A time-varying bivariate copula joint model for longitudinal and time-to-event data
论文作者
论文摘要
本文提出了一个随机效应和随机效应和随时间变化的双变量copulas共同测量的纵向结果，该模型在每个时间点重复测量了纵向结果。常规的联合模型通常认为存在特定于主体的潜在随机效应或由纵向和事件时间过程共享的类别，并且考虑到这些潜在变量，这两个过程在条件上是独立的。在这一假设下，这两个过程的联合可能性是直接得出的，并且它们之间的关联以及人群中的异质性自然是由不可观察的潜在变量自然引入的。但是，由于这些潜在变量的不可观察的性质，因此难以验证条件的独立性假设。因此，除了随机效应外，还引入了随机变化的双变量copula，以说明两个过程之间的额外时间依赖性关联。提出的模型包括常规联合模型作为某些Copulas下的特殊情况。仿真研究表明，所提出的模型中的参数估计量具有鲁棒性针对Copula错误指定，并且与常规关节模型相比，它在预测生存概率方面具有较高的性能。对主要的胆道肝硬化（PBC）数据进行了实际数据应用。
A time-varying bivariate copula joint model, which models the repeatedly measured longitudinal outcome at each time point and the survival data jointly by both the random effects and time-varying bivariate copulas, is proposed in this paper. A regular joint model normally supposes there exist subject-specific latent random effects or classes shared by the longitudinal and time-to-event processes and the two processes are conditionally independent given these latent variables. Under this assumption, the joint likelihood of the two processes is straightforward to derive and their association, as well as heterogeneity among the population, are naturally introduced by the unobservable latent variables. However, because of the unobservable nature of these latent variables, the conditional independence assumption is difficult to verify. Therefore, besides the random effects, a time-varying bivariate copula is introduced to account for the extra time-dependent association between the two processes. The proposed model includes a regular joint model as a special case under some copulas. Simulation studies indicates the parameter estimators in the proposed model are robust against copula misspecification and it has superior performance in predicting survival probabilities compared to the regular joint model. A real data application on the Primary biliary cirrhosis (PBC) data is performed.
